The document discusses the CycleTracks project, which collected GPS data from cyclists in San Francisco to understand their route choices. It summarizes who participated in CycleTracks and the types of data collected. The data was then used to estimate a bike route choice model and update SF-CHAMP, the travel demand model, to better represent bike travel and predict the effects of bike infrastructure projects.
Completing the Cycle: Incorporating CycleTracks into SF-CHAMP
1. Completing the Cycle:
Incorporating CycleTracks
into SF-CHAMP
Using technology to understand the needs
of cyclists
SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY
Fall 2012
2. Outline
1. Why make CycleTracks?
2. What does CycleTracks do?
3. Who used CycleTracks and why?
4. What data did we get from CycleTracks?
5. What did we do with that data?
6. Evolution and future of CycleTracks
4. Why CycleTracks?
Need to prioritize projects, including bike projects.
Estimate a bike choice model that evaluated
various bike infrastructure features
Needed bike route choice data on a budget.
16. SF Participants: Fall 2009 to Spring 2010
CycleTracks BATS
N-366 N=153 z-stat
Age
Mean 34 33 1.1
Gender
Female 21% 36% -3.5
Cycling Frequency
Daily 60%
Several Times/Week 34%
Several Times/Month 7%
Less than once a month 0% N/A
17. 4. What data did we get?
- Data Quality
- Data Summaries
25. …as well as to a set of routes that were not
chosen
26. What makes us choose one bike route over
another ?
Personal Trip
Info Features
Route
Which route
Features of
was
Available Route chosen?
Routes
Choice
Model
28. Average Marginal Rates of Substitution
MRS of Length on Street for Value Units
Turns 0.10 mi/turn
Total Rise 1.12 mi/100ft
Length Wrong Way 4.02 None
Length on Bike Paths 0.57 None
Length on Bike Lanes 0.49 None
Length on Bike Routes 0.92 None
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29. Updates to SF-CHAMP
Synthesized Core, 3 iterations
Population
Work Location,
Land Use Destination Choice,
Mode Choice Tour Generation
Networks
Networks Logsums
+Bike Vars! Tour & Trip
Mode Choice
Bike Route Choice Set Road & Transit
Non-Motorized Bike
Generation & Assignment/
Skimming (Distances) Logsums
Skimming Skimming
Initial Road & Transit
Assignment/
Skimming
Final Bicycle
Assignment
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29
32. Bike Logsums: From 4th and King
Effect of Bike Plan Build
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33. Bike Logsums: To 4th and King
Effect of Bike Plan Build
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34. Preliminary Results:
Tour Mode Choice Sensitivity
Tour Difference
Daily Tours v4.1 Harold v4.3 Fury
Bike 300 0.1% 1,300 0.9%
Walk 300 0.0% 200 0.0%
Transit 200 0.0% -900 -0.1%
Auto -1,000 -0.0% -600 -0.0%
Total -200 -0.0% 0 0.0%
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35. Preliminary Results:
Trip Mode Choice Sensitivity
Trip Difference
Daily Tours v4.1 Harold v4.3 Fury
Bike 500 0.1% 3,000 0.8%
Walk 1,100 0.0% -500 -0.0%
Transit 850 0.0% -600 -0.0%
Auto -2,400 -0.0% -1,300 -0.0%
Total 0 0.0% 600 0.0%
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45. Bike Logsums: From Inner Sunset
Effect of Bike Plan Build
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46. Bike Logsums: To Inner Sunset
Effect of Bike Plan Build
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Notas del editor
Here is a comparison of the demographics of the participants whose traces survived the data processing to the subpopulation from the Bay Area Travel Survey that reported a cycling trip in San Francisco. As you can see, the CycleTracks sample is over twice the size of BATS, which contained 50,000 households, illustrating why seeking a representative sample to study cycling is not feasible. But, our sample is biased. While the mean age in the two samples are not significantly different, our study does include a lower proportion of women at 21% compared to BATS’ 36%. While we don’t have a population to compare cycling frequency against, we also suspect that our sample is biased toward frequent cyclists. The bias is a limited problem because we were able to account for it with interaction variables in model estimation.
Schuessler, Nadine and Kay W. Axhausen. “Processing Raw Data from Global Positioning Systems Without Additional Information,” Transportation Research Record : Journal of the Transportation Research Board, No 2105. Washington D.C., 2009, pp. 28-35. http://trb.metapress.com/content/tv306m812140p330/
SharrowsCongestionNight-timeNo bike lanesCapacity of roadwayBike laneBike pathCrimeWeather
We use a “Doubly Stochastic Route Search” to find other potential routes in the available choice setBovy, P. & Fiorenzo-Catalano, S. (2007), “Stochastic route choice set generation: behavioral and probabilistic foundations,” Transportmetrica 3, 173-189.
Here are the coefficients from the path size multinomial logit route choice model. Obviously, cyclists prefer shorter routes, with fewer turns, and don’t go the wrong way down a one way street unneccessarily. The coeficients on the proportions of the different bicycle facility types are measured on the same scale, and so represent the relative preferences for these treatments. Bike lanes are preferred the most, especially by infrequent cyclists, a preliminary indication that installing bicycle lanes may attract new cyclists. Hill climbing is especially disfavored by women and on commute trips. The path size variable corrects for the correlation between alternatives due to route overlap. The coefficient is not significantly different from the theoretically correct value in a model with a scale parameter of one, another indication of the quality with which our choice sets represent the consideration sets. Traffic volume, vehicle speed, number of lanes, crime, and rain had no effect.
The average cyclist will Avoid a turn if it costs no more than one-tenth of a mileAvoid climbing a hill 100 feet tall as long as the detour is less than roughly one mileAvoid traveling the wrong way down a one-way street unless doing so saves more than four times the distance elsewhereAdd a mile on bike lanes in exchange for only half a mile on ordinary roadsBike paths vs Bike Lanes could be due to limited off-street bike path facilities in SF and other factors that make them less attractive (although Krizek also found similar preferences).Mention the other variables that were significant in estimation: females and work-commuters were more hill-averse
Talking points/or circle/or flip through
Yellow doesn’t stand out enough
Not apples to apples: the bike network coded in Harold was much more aggressive. That said, the results are still interesting:Harold has an auto-stick, but no carrot, so the mode switchers switch to whatever is best for them, which happens to be pretty even across bike, walk and transit.Fury has a very strong bike-carrot, and walk modes benefit mildly from the road diets. Biking draws from auto but also transit because of tour distance.
Harold again has the auto stick. Walk trips are up because of transit tours being up.Fury has the bike carrot. Walk trips are down (despite the walk tours being up) because of the transit tours being down – many walk trips are part of a transit tour.
Our code is open source, and there are a number of agencies who have tried their hand at modifying it to their own needs.